Degrees and courses in Data Science

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A year ago, I saw this announcement in the Harvard Yard:

Right now (May 2019) undergraduates at Harvard still can't major in Data Science, but they can take related courses in departments including Statistics, Computer Science and - as the photo proves - Government.

Interestingly, if you are high school student you could think about pursuing one of the following:

  • A Bachelor of Science in Computational and Data Sciences from George Mason University.
  • A Data Science and Analytics B.S. from East Michigan University.
  • BA in Data Science at Columbia.
  • A Data Science degree from BYU which apparently costs just $2,009 per semester for LDS students and $4,018 per semester for non-LDS student.
  • An Intercollege Undergraduate Major in Data Sciences from Pennsylvania State University, a terrific deal especially if you are a Pennsylvania resident.
  • A Data Science degree from UCSD also looks like fine choice, although currently the subject domain courses are all from natural sciences (Biological Sciences, Chemistry, and Physics), so this would currently not be a good fit if you plan to become a data journalist covering economic or political topics.

And there are dozens of similar options out there for undergraduates, although the institutions I am most familiar with do not offer in BA/BS degrees in data science right now:

Snippet from the NYU course catalog
Snippet from the Harvard course catalog

Prospective graduate students do have options at both Harvard and NYU:

  • Master of Science in Data Science at the John A. Paulson School of Engineering and Applied Sciences (SEAS)
  • Master of Health Data Science at Harvard T.H. Chan School of Public Health
  • MS in Data Science at NYU
  • A fairly new, exciting option: PhD in Data Science at the NYU Center for Data Science.

Who should study data science?

In a viral thread Rochelle Terman asked a few days "When teaching R, how far to you go? Should you cover object-oriented programming, compiling in C, assembly, electric circuits and motherboards?"

2) What is the end goal of methods training? Which people deserve to be taught? What is the intention behind teaching "technical details", and how does it actually function with our students? 17/n— Rochelle Terman (@RochelleTerman) May 10, 2019

In my opinion, the beauty of many data science courses is precisely that some technical aspects are skipped when there isn't a clear a pedagogical justification for them.

What Terman describes as computational social science (CSS) encapsulates proficiency in R, python, git, webscraping, web development, machine learning, text-as-data. Add that visualization and story-telling, and CSS starts to look a lot like data science. A very valuable set of skills, in my opinion.

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